Motion Retargetting based on Dilated Convolutions and Skeleton-specific Loss Functions

Sang Bin Kim, Inbum Park, Seongsu Kwon, Jung Hyun Han

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Motion retargetting refers to the process of adapting the motion of a source character to a target. This paper presents a motion retargetting model based on temporal dilated convolutions. In an unsupervised manner, the model generates realistic motions for various humanoid characters. The retargetted motions not only preserve the high-frequency detail of the input motions but also produce natural and stable trajectories despite the skeleton size differences between the source and target. Extensive experiments are made using a 3D character motion dataset and a motion capture dataset. Both qualitative and quantitative comparisons against prior methods demonstrate the effectiveness and robustness of our method.

Original languageEnglish
Pages (from-to)497-507
Number of pages11
JournalComputer Graphics Forum
Volume39
Issue number2
DOIs
Publication statusPublished - 2020 May 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (NRF‐2017M3C4A7066316 and No. NRF2016‐R1A2B3014319).

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (NRF-2017M3C4A7066316 and No. NRF2016-R1A2B3014319).

Publisher Copyright:
© 2020 The Author(s) Computer Graphics Forum © 2020 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

Keywords

  • CCS Concepts
  • • Computing methodologies → Neural networks

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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